Bathymetry Prediction With SWOT Gravity Anomaly Using Machine Learning Methods: Paper 1–Model Development
TL;DR
Five independent research groups are presented, each incorporating the fundamental physics of downward continuation within advanced data‐driven modeling frameworks, and each provides a substantial improvement in depth accuracy at shallow seamounts and deep trenches.
Abstract
Only one quarter of the global ocean floor has been directly surveyed; the remaining three quarters are inferred from satellite altimeter‐derived gravity data using techniques developed in the 1990s. These classical methods correlate gravity anomalies with known depths and extrapolate bathymetry in unsounded regions. However, spatial resolution has remained limited to ∼ ${\sim} $ 12 km full wavelength due to the smoothing effects of upward continuation. Two recent advances are now reshaping this field: the Surface Water and Ocean Topography mission has increased radar altimeter range precision by a factor of four, effectively doubling the two‐dimensional resolution, and modern machine learning (ML) approaches allow for scalable, high‐fidelity inversion. The authors of this paper organized a workshop, initially at the Technical University of Denmark, that brought together experts in marine gravity, seafloor mapping, and ML to tackle this challenge. Here, we present results from five independent research groups demonstrating consistent and substantial improvements (23%–46%) in bathymetric prediction accuracy, each incorporating the fundamental physics of downward continuation within advanced data‐driven modeling frameworks. In addition to improving the accuracy and resolution of the predicted bathymetry, each of the five models provides a substantial improvement in depth accuracy at shallow seamounts and deep trenches.